Data communication assistance via swarm robotic: a behaviour creation comparison

This paper compares two behaviour creation algorithms for Unmanned Aerial Vehicle swarms. The objective of this work is to have a self-organising robotic swarm which unintrusively assists humans in network restricted environments by facilitating data-transfer between disconnected groups. The behaviour creation algorithms explored are a well-cited approach for evolving neural networks (ENN) and our Learning Classifier System (LCS) approach. We utilise simulations in randomised, obstacle dense landscapes to validate, compare and contrast these two algorithms. We explore the algorithms both with and without the environment layout being known to the swarm a priori. These simulations show the ENN algorithm struggles to create appropriate behaviours for this complex data-transfer task, resulting in an unassertive swarm. Our LCS behaviours have the swarm operate with up to 61% of the fitness range above ENN in both expected and unexpected environments, resulting in the desired human assistance.